Leveraging ML for combinatorial optimization (MLCO) has attracted significant attention in recent years [1]}, [2]}, [3]}, [4]}. In their survey, [2]} have classified MLCO studies into three categories depending on the role of ML: (1) end-to-end learning in which ML is used to directly produce solutions for CO problem instances, (2) learning to configure algorithm in which ML is used to efficiently parameterise optimization algorithms (in a broad sense), (3) ML alongside optimization algorithms in which ML is used to assist low-level decisions within optimization algorithms. In the operations research and computational intelligence community, MLCO has been investigated extensively and referred to as hyper-heuristics [6]}, [7]}, [8]}, [9]}, [10]}. Different from traditional search algorithms that attempt to find good solutions in the solution search space, hyper-heuristics emphasise on exploring the heuristic (or algorithm) search space to find a good way to solve problem instances. Hyper-heuristics can be used for heuristic generation or heuristic selection. In heuristic generation, hyper-heuristics use a search algorithm (e.g. GP) to generate/discover effective heuristics for a problem subset. Generated heuristics can be used to construct solutions or refine/improve a solution (similar to the above mentioned categories 1 and 2 in [2]}). When used for heuristic selection, the goal of hyper-heuristics is to adaptively select low-level heuristics in optimization algorithms to solve problem instances (similar to category 3 in [2]}). To avoid confusion, we will use [2]}'s categorisation to discuss related work.
